Python作业6--中国大学排名数据分析与可视化、豆瓣图书评论数据分析与可视化
import requests from bs4 import BeautifulSoup as bs import pandas as pd from matplotlib import pyplot as plt def get_rank(url): count = 0 rank = [] headers = { "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.54 Safari/537.36 Edg/101.0.1210.3" } resp = requests.get(url, headers=headers).content.decode() soup = bs(resp, "lxml") univname = soup.find_all('a', class_="name-cn") for i in univname: if count != 10: university = i.text.replace(" ", "") score = soup.select("#content-box > div.rk-table-box > table > tbody > tr:nth-child({}) > td:nth-child(5)" .format(count + 1))[0].text.strip() rank.append([university, score]) else: break count += 1 return rank total = [] u_year = 2015 for i in range(15, 20): url = "https://www.shanghairanking.cn/rankings/bcur/20{}11".format(i) print(url) title = ['学校名称', '总分'] df = pd.DataFrame(get_rank(url), columns=title) total.append(df) for i in total: plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 x = list(i["学校名称"])[::-1] y = list(i["总分"])[::-1] # 1.创建画布 plt.figure(figsize=(20, 8), dpi=100) # 2.绘制图像 plt.plot(x, y, label="大学排名") # 2.2 添加网格显示 plt.grid(True, linestyle="--", alpha=0.5) # 2.3 添加描述信息 plt.xlabel("大学名称") plt.ylabel("总分") plt.title(str(u_year) + "年软科中国最好大学排名Top10", fontsize=20) # 2.5 添加图例 plt.legend(loc="best") # 3.图像显示 plt.savefig(str(u_year)+".png") plt.show() u_year += 1 while True: info = input("输入学校名称和年份：") count = 0 university, year = info.split() year = int(year) judge = 2019 - year tmp = total[::-1] if 4 >= judge >= 0: name = list(total[judge - 1]["学校名称"]) for j in name: if university == j: print(university + "在{0}年排名第{1}".format(year, count + 1)) break count += 1 if count ==10: print("很抱歉，没有该学校的排名记录！！！") print("请选择以下选项:") print(" 1.继续查询") print(" 2.结束查询") select = int(input("")) if select == 1: continue elif select == 2: break else: break else: print("很抱歉，没有该年份的排名记录！！！") print("请选择以下选项:") print(" 1.继续查询") print(" 2.结束查询") select = int(input("")) if select == 1: continue elif select == 2: break
豆瓣图书评论数据分析与可视化
import re from collections import Counter import requests # from lxml import etree from lxml import etree import pandas as pd import jieba import matplotlib.pyplot as plt from wordcloud import WordCloud headers = { # "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.54 Safari/537.36 Edg/101.0.1210.39" "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.0.0 Safari/537.36" } comments = [] words = [] def regex_change(line): # 前缀的正则 username_regex = re.compile(r"^\d+::") # URL，为了防止对中文的过滤，所以使用[a-zA-Z0-9]而不是\w url_regex = re.compile(r""" (https?://)? ([a-zA-Z0-9]+) (\.[a-zA-Z0-9]+) (\.[a-zA-Z0-9]+)* (/[a-zA-Z0-9]+)* """, re.VERBOSE | re.IGNORECASE) # 剔除日期 data_regex = re.compile(u""" #utf-8编码 年 | 月 | 日 | (周一) | (周二) | (周三) | (周四) | (周五) | (周六) """, re.VERBOSE) # 剔除所有数字 decimal_regex = re.compile(r"[^a-zA-Z]\d+") # 剔除空格 space_regex = re.compile(r"\s+") regEx = "[\n”“|,，；;''/?! 。的了是]" # 去除字符串中的换行符、中文冒号、|，需要去除什么字符就在里面写什么字符 line = re.sub(regEx, "", line) line = username_regex.sub(r"", line) line = url_regex.sub(r"", line) line = data_regex.sub(r"", line) line = decimal_regex.sub(r"", line) line = space_regex.sub(r"", line) return line def getComments(url): score = 0 resp = requests.get(url, headers=headers).text html = etree.HTML(resp) comment_list = html.xpath(".//div[@class='comment']") for comment in comment_list: status = "" name = comment.xpath(".//span[@class='comment-info']/a/text()")[0] # 用户名 content = comment.xpath(".//p[@class='comment-content']/span[@class='short']/text()")[0] # 短评内容 content = str(content).strip() word = jieba.cut(content, cut_all=False, HMM=False) time = comment.xpath(".//span[@class='comment-info']/a/text()")[1] # 评论时间 mark = comment.xpath(".//span[@class='comment-info']/span/@title") # 评分 if len(mark) == 0: score = 0 else: for i in mark: status = str(i) if status == "力荐": score = 5 elif status == "推荐": score = 4 elif status == "还行": score = 3 elif status == "较差": score = 2 elif status == "很差": score = 1 good = comment.xpath(".//span[@class='comment-vote']/span[@class='vote-count']/text()")[0] # 点赞数（有用数） comments.append([str(name), content, str(time), score, int(good)]) for i in word: if len(regex_change(i)) >= 2: words.append(regex_change(i)) def getWordCloud(words): # 生成词云 all_words = [] all_words += [word for word in words] dict_words = dict(Counter(all_words)) bow_words = sorted(dict_words.items(), key=lambda d: d[1], reverse=True) print("热词前10位：") for i in range(10): print(bow_words[i]) text = ' '.join(words) w = WordCloud(background_color='white', width=1000, height=700, font_path='simhei.ttf', margin=10).generate(text) plt.show() plt.imshow(w) w.to_file('wordcloud.png') print("请选择以下选项:") print(" 1.热门评论") print(" 2.最新评论") info = int(input()) print("前10位短评信息：") title = ['用户名', '短评内容', '评论时间', '评分', '点赞数'] if info == 1: comments = [] words = [] for i in range(0, 60, 20): url = "https://book.douban.com/subject/10517238/comments/?start={}&limit=20&status=P&sort=new_score".format( i) # 前3页短评信息（热门） getComments(url) df = pd.DataFrame(comments, columns=title) print(df.head(10)) print("点赞数前10位的短评信息：") df = df.sort_values(by='点赞数', ascending=False) print(df.head(10)) getWordCloud(words) elif info == 2: comments = [] words=[] for i in range(0, 60, 20): url = "https://book.douban.com/subject/10517238/comments/?start={}&limit=20&status=P&sort=time".format( i) # 前3页短评信息（最新） getComments(url) df = pd.DataFrame(comments, columns=title) print(df.head(10)) print("点赞数前10位的短评信息：") df = df.sort_values(by='点赞数', ascending=False) print(df.head(10)) getWordCloud(words)
